code: def iou_pred(boxes, box_deltas):
      """
      Transform the set of class-agnostic boxes into class-specific boxes
      by applying the predicted offsets (box_deltas)
      :param boxes: !important [N 4]
      :param box_deltas: [N, 4 * num_classes]
      :return: [N 4 * num_classes]
      """
      if boxes.shape[0] == 0:
          return np.zeros((0, box_deltas.shape[1]))
  
      boxes = boxes.astype(np.float, copy=False)
      x1 = boxes[:, 0]
      y1 = boxes[:, 1]
      x2 = boxes[:, 2]
      y2 = boxes[:, 3]
  
      dx1 = box_deltas[:, 0::4]
      dy1 = box_deltas[:, 1::4]
      dx2 = box_deltas[:, 2::4]
      dy2 = box_deltas[:, 3::4]
  
      pred_boxes = np.zeros(box_deltas.shape)
      # x1
      pred_boxes[:, 0::4] = dx1 + x1[:, np.newaxis]
      # y1
      pred_boxes[:, 1::4] = dy1 + y1[:, np.newaxis]
      # x2
      pred_boxes[:, 2::4] = dx2 + x2[:, np.newaxis]
      # y2
      pred_boxes[:, 3::4] = dy2 + y2[:, np.newaxis]
  
      return pred_boxes
  
file_path: insightface/detection/retinaface_anticov/rcnn/processing/bbox_transform.py
imports: import numpy
  from ..cython.bbox import bbox_overlaps_cython
label_desc: Use image node_id 9 for calling a global function with example usage: iou_pred(boxes, box_deltas) and returns: pred_boxes, np